Games in Networks: the price of anarchy and learning

Date and Time

Thursday, December 11, 2008 - 4:30pm to 6:00pm

Location

Computer Science Small Auditorium (Room 105)

Type

Colloquium

Speaker

Eva Tardos, from Cornell

Host

Sanjeev Arora

Network games play a fundamental role in understanding behavior in many
domains, ranging from communication networks through markets to social
networks. Such networks are used, and also evolve due to selfish behavior of
the users and owners. In light of these competing forces, it is surprising
how efficient these networks are. It is an exciting challenge to understand
the operation and success of these networks in game theoretic terms: what
principles of interaction lead selfish participants to form such efficient
networks?
We will focus on congestion games, and study the degradation of quality of
solution caused by the selfish behavior of users. We model users as learning
algorithms, and show that natural learning behavior can avoid bad outcomes
predicted by the price of anarchy in atomic congestion games such as the
load-balancing game. We use tools from the theory of dynamical systems and
algebraic geometry to show when players use a class of natural learning
algorithms the distribution of play converges to the set of weakly stable
equilibria, and that the set of weakly stable equilibria are the pure Nash
equilibria with probability 1 when congestion costs are selected at random
independently on each edge (from any monotonically parametrized
distribution).
The talk is a survey and self-contained.
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About the speaker: Eva Tardos is a Professor of Computer Science at Cornell University where she is currently chair.
Her research is in Algorithm Design and Algorithmic Game Theory. Algorithmic game theory is an emerging new area of designing systems and algorithms for selfish users.
She is a winner of the Fulkerson Prize and the Dantzig Prize.